Deep learning methods, which are known for their capability to process massive amounts of data and are relatively robust against noise are the best methods for intelligent fault detection [1]}. Convolutional Neural Networks (CNN) [2]}, [3]}, Stacked Auto Encoders [4]}, [5]}, and Deep Belief Networks (DBN)[6]} are among the most studied algorithms that could reach very high accuracy. Industrial machines mainly operate in normal conditions, so there are more normal data than fault data that makes the available data imbalanced. Even though some methods like one-class classification and novelty detection can detect faults in such conditions, identifying the type of faults is not possible [7]}. To solve this problem, Generative algorithms can be employed to generate fault data. Generative algorithms are unsupervised learning paradigms that automatically discover patterns in input data so the model can produce new examples. Variational Autoencoders (VAE) and Generative Adversarial Networks (GAN) are the most famous generative models and have been wildly used for bearing fault detection [8]}, [9]}, and data augmentation [10]}. CGAN is a variation of GAN, that can generate conditional new data [11]}. In [12]}, where datasets are limited and imbalanced, a conditional deep convolutional generative adversarial network is used for machine fault diagnosis. Yin et al. [13]} also applied a data generation method based on Wasserstein generative adversarial network and convolutional neural network (WG-CNN) for bearing fault detection. These methods aim to extract the input data's probability distribution and hidden information so that they can be sampled and used to generate new data. Moreover, the distribution of any condition is unique, so the distributions of unknown conditions can be found with the information of known ones and used to generate data for new conditions where fault data is not available.
